Finally, there is a growing recent body of work studying various aspects of interactions between strategic players and learning algorithms, including learning from strategic data [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, security games [9]}, [10]}, [11]}, and optimization against regret minimizing opponents [12]}, [13]}. Notably, we use the formalism of mean-based learning algorithms defined in [13]}, who also study an auction setting. Their setting, however, is basically different from ours. They consider a game in which a strategic seller seeks revenue maximizing auction strategies against a regret minimizing buyer, while we consider fixed auction rules and study the interactions among regret-minimizing buyers and the strategic consideration of their users.
Lastly,
in a companion paper we formalize a “meta-game” model for strategic settings in which human users select parameters to input into learning agents that play a repeated game on their behalf, and analyze this model for normal-form games.
Our discussion on the incentives of players to mis-report their valuation to their own bidding agents is
directly related to this model.
